{"title":"基于智能视频分析的跌倒检测研究","authors":"Y. T. Ngo, Nguyen Ha Thanh, T. V. Pham","doi":"10.1109/ATC.2012.6404242","DOIUrl":null,"url":null,"abstract":"In this paper, a fall detection algorithm has been built using intelligent analysis of captured video signal. Five geometrical features are extracted from input video signal and are recognized by a trained feed-forward neural network. Experimental results on our self-built database show that the proposed fall detection system can detect fall events with quite high precision under different falling conditions.","PeriodicalId":282211,"journal":{"name":"The 2012 International Conference on Advanced Technologies for Communications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Study on fall detection based on intelligent video analysis\",\"authors\":\"Y. T. Ngo, Nguyen Ha Thanh, T. V. Pham\",\"doi\":\"10.1109/ATC.2012.6404242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a fall detection algorithm has been built using intelligent analysis of captured video signal. Five geometrical features are extracted from input video signal and are recognized by a trained feed-forward neural network. Experimental results on our self-built database show that the proposed fall detection system can detect fall events with quite high precision under different falling conditions.\",\"PeriodicalId\":282211,\"journal\":{\"name\":\"The 2012 International Conference on Advanced Technologies for Communications\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2012 International Conference on Advanced Technologies for Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATC.2012.6404242\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2012 International Conference on Advanced Technologies for Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATC.2012.6404242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on fall detection based on intelligent video analysis
In this paper, a fall detection algorithm has been built using intelligent analysis of captured video signal. Five geometrical features are extracted from input video signal and are recognized by a trained feed-forward neural network. Experimental results on our self-built database show that the proposed fall detection system can detect fall events with quite high precision under different falling conditions.
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